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  <h1>Source code for cdt.causality.pairwise.GNN</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;GNN : Generative Neural Networks for causal inference (pairwise).</span>

<span class="sd">Authors : Olivier Goudet &amp; Diviyan Kalainathan</span>
<span class="sd">Ref: Causal Generative Neural Networks (https://arxiv.org/abs/1711.08936)</span>
<span class="sd">Date : 10/05/2017</span>

<span class="sd">.. MIT License</span>
<span class="sd">..</span>
<span class="sd">.. Copyright (c) 2018 Diviyan Kalainathan</span>
<span class="sd">..</span>
<span class="sd">.. Permission is hereby granted, free of charge, to any person obtaining a copy</span>
<span class="sd">.. of this software and associated documentation files (the &quot;Software&quot;), to deal</span>
<span class="sd">.. in the Software without restriction, including without limitation the rights</span>
<span class="sd">.. to use, copy, modify, merge, publish, distribute, sublicense, and/or sell</span>
<span class="sd">.. copies of the Software, and to permit persons to whom the Software is</span>
<span class="sd">.. furnished to do so, subject to the following conditions:</span>
<span class="sd">..</span>
<span class="sd">.. The above copyright notice and this permission notice shall be included in all</span>
<span class="sd">.. copies or substantial portions of the Software.</span>
<span class="sd">..</span>
<span class="sd">.. THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span>
<span class="sd">.. IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span>
<span class="sd">.. FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span>
<span class="sd">.. AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span>
<span class="sd">.. LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span>
<span class="sd">.. OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span>
<span class="sd">.. SOFTWARE.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span> <span class="k">as</span> <span class="nn">th</span>
<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">trange</span>
<span class="kn">from</span> <span class="nn">pandas</span> <span class="kn">import</span> <span class="n">DataFrame</span>
<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">TensorDataset</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span>
<span class="kn">from</span> <span class="nn">.model</span> <span class="kn">import</span> <span class="n">PairwiseModel</span>
<span class="kn">from</span> <span class="nn">...utils.loss</span> <span class="kn">import</span> <span class="n">MMDloss</span>
<span class="kn">from</span> <span class="nn">...utils.Settings</span> <span class="kn">import</span> <span class="n">SETTINGS</span>
<span class="kn">from</span> <span class="nn">...utils.parallel</span> <span class="kn">import</span> <span class="n">parallel_run</span>
<span class="kn">from</span> <span class="nn">...utils.io</span> <span class="kn">import</span> <span class="n">MetaDataset</span>


<div class="viewcode-block" id="GNN_model"><a class="viewcode-back" href="../../../../models.html#cdt.causality.pairwise.GNN_model">[docs]</a><span class="k">class</span> <span class="nc">GNN_model</span><span class="p">(</span><span class="n">th</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Torch model for the GNN structure.</span>

<span class="sd">    Args:</span>
<span class="sd">        batch_size (int): size of the batch going to be fed to the model</span>
<span class="sd">        nh (int): Number of hidden units in the hidden layer</span>
<span class="sd">        lr (float): Learning rate of the Model</span>
<span class="sd">        train_epochs (int): Number of train epochs</span>
<span class="sd">        test_epochs (int): Number of test epochs</span>
<span class="sd">        idx (int): Index (for printing purposes)</span>
<span class="sd">        verbose (bool): Verbosity of the model</span>
<span class="sd">        dataloader_workers (int): Number of workers for dataset loading</span>
<span class="sd">        device (str): device on with the algorithm is going to be run on</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">nh</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">train_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
                 <span class="n">test_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">dataloader_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Build the Torch graph.</span>

<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">GNN_model</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">l1</span> <span class="o">=</span> <span class="n">th</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">nh</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">l2</span> <span class="o">=</span> <span class="n">th</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">nh</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;noise&#39;</span><span class="p">,</span> <span class="n">th</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">th</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">criterion</span> <span class="o">=</span> <span class="n">MMDloss</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">th</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">l1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">l2</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">train_epochs</span> <span class="o">=</span> <span class="n">train_epochs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">test_epochs</span> <span class="o">=</span> <span class="n">test_epochs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">SETTINGS</span><span class="o">.</span><span class="n">get_default</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dataloader_workers</span> <span class="o">=</span> <span class="n">dataloader_workers</span>

<div class="viewcode-block" id="GNN_model.forward"><a class="viewcode-back" href="../../../../models.html#cdt.causality.pairwise.GNN_model.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Pass data through the net structure.</span>
<span class="sd">        Args:</span>
<span class="sd">            x (torch.Tensor): input data: shape (:,1)</span>

<span class="sd">        Returns:</span>
<span class="sd">            torch.Tensor: Output of the shallow net</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">noise</span><span class="o">.</span><span class="n">normal_</span><span class="p">()</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">th</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">noise</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span></div>

<div class="viewcode-block" id="GNN_model.run"><a class="viewcode-back" href="../../../../models.html#cdt.causality.pairwise.GNN_model.run">[docs]</a>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Run the GNN on a pair x,y of FloatTensor data.</span>

<span class="sd">        Args:</span>
<span class="sd">            dataset (torch.utils.data.Dataset): True data; First element is the cause</span>

<span class="sd">        Returns:</span>
<span class="sd">            torch.Tensor: Score of the configuration</span>

<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">optim</span> <span class="o">=</span> <span class="n">th</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lr</span><span class="p">)</span>
        <span class="n">teloss</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">pbar</span> <span class="o">=</span> <span class="n">trange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_epochs</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_epochs</span><span class="p">,</span>
                      <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">)</span>
        <span class="n">dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
                                <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">drop_last</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                                <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataloader_workers</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">pbar</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span>
                <span class="n">optim</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
                <span class="n">pred</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
                <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">criterion</span><span class="p">(</span><span class="n">th</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">pred</span><span class="p">],</span> <span class="mi">1</span><span class="p">),</span> <span class="n">th</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
                <span class="k">if</span> <span class="n">epoch</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_epochs</span><span class="p">:</span>
                    <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
                    <span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="n">teloss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">data</span>

                <span class="c1"># print statistics</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">epoch</span> <span class="o">%</span> <span class="mi">50</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                    <span class="n">pbar</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">(</span><span class="n">idx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">idx</span><span class="p">,</span> <span class="n">score</span><span class="o">=</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>

        <span class="k">return</span> <span class="n">teloss</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_epochs</span></div>

    <span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;reset_parameters&quot;</span><span class="p">):</span>
                <span class="n">layer</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span></div>


<span class="k">def</span> <span class="nf">GNN_instance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nh</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Run an instance of GNN, testing causal direction.</span>

<span class="sd">    :param m: data corresponding to the config : (N, 2) data, [:, 0] cause and [:, 1] effect</span>
<span class="sd">    :param pair_idx: print purposes</span>
<span class="sd">    :param run: numner of the run (for GPU dispatch)</span>
<span class="sd">    :param device: device on with the algorithm is going to be run on.</span>
<span class="sd">    :return:</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">batch_size</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="fm">__len__</span><span class="p">()</span>
    <span class="n">device</span> <span class="o">=</span> <span class="n">SETTINGS</span><span class="o">.</span><span class="n">get_default</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
    <span class="n">GNNXY</span> <span class="o">=</span> <span class="n">GNN_model</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">nh</span><span class="o">=</span><span class="n">nh</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">GNNYX</span> <span class="o">=</span> <span class="n">GNN_model</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">nh</span><span class="o">=</span><span class="n">nh</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">GNNXY</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
    <span class="n">GNNYX</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">Dataset</span><span class="p">):</span>
        <span class="n">XY</span> <span class="o">=</span> <span class="n">GNNXY</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
        <span class="n">YX</span> <span class="o">=</span> <span class="n">GNNYX</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">XY</span> <span class="o">=</span> <span class="n">GNNXY</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">TensorDataset</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)))</span>
        <span class="n">YX</span> <span class="o">=</span> <span class="n">GNNYX</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">TensorDataset</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)))</span>
    <span class="k">return</span> <span class="p">[</span><span class="n">XY</span><span class="p">,</span> <span class="n">YX</span><span class="p">]</span>


<div class="viewcode-block" id="GNN"><a class="viewcode-back" href="../../../../causality.html#cdt.causality.pairwise.GNN">[docs]</a><span class="k">class</span> <span class="nc">GNN</span><span class="p">(</span><span class="n">PairwiseModel</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Shallow Generative Neural networks.</span>

<span class="sd">    **Description:** Pairwise variant of the CGNN approach,</span>
<span class="sd">    Models the causal directions x-&gt;y and y-&gt;x with a 1-hidden layer neural</span>
<span class="sd">    network and a MMD loss. The causal direction is considered as the best-fit</span>
<span class="sd">    between the two causal directions.</span>

<span class="sd">    **Data Type:** Continuous</span>

<span class="sd">    **Assumptions:** The class of generative models is not restricted with a</span>
<span class="sd">    hard contraint, but with the hyperparameter ``nh``. This algorithm greatly</span>
<span class="sd">    benefits from bootstrapped runs (nruns &gt;=12 recommended), and is very</span>
<span class="sd">    computationnally heavy. GPUs are recommended.</span>

<span class="sd">    Args:</span>
<span class="sd">        nh (int): number of hidden units in the neural network</span>
<span class="sd">        lr (float): learning rate of the optimizer</span>
<span class="sd">        nruns (int): number of runs to execute per batch</span>
<span class="sd">           (before testing for significance with t-test).</span>
<span class="sd">        njobs (int): number of runs to execute in parallel.</span>
<span class="sd">           (defaults to ``cdt.SETTINGS.NJOBS``)</span>
<span class="sd">        gpus (bool): Number of available gpus</span>
<span class="sd">           (defaults to ``cdt.SETTINGS.GPU``)</span>
<span class="sd">        idx (int): (optional) index of the pair, for printing purposes</span>
<span class="sd">        verbose (bool): verbosity (defaults to ``cdt.SETTINGS.verbose``)</span>
<span class="sd">        batch_size (int): batch size, defaults to full-batch</span>
<span class="sd">        train_epochs (int): Number of epochs used for training</span>
<span class="sd">        test_epochs (int): Number of epochs used for evaluation</span>
<span class="sd">        dataloader_workers (int): how many subprocesses to use for data</span>
<span class="sd">           loading. 0 means that the data will be loaded in the main</span>
<span class="sd">           process. (default: 0)</span>

<span class="sd">    .. note::</span>
<span class="sd">       Ref : Learning Functional Causal Models with Generative Neural Networks</span>
<span class="sd">       Olivier Goudet &amp; Diviyan Kalainathan &amp; Al.</span>
<span class="sd">       (https://arxiv.org/abs/1709.05321)</span>

<span class="sd">    Example:</span>
<span class="sd">        &gt;&gt;&gt; from cdt.causality.pairwise import GNN</span>
<span class="sd">        &gt;&gt;&gt; import networkx as nx</span>
<span class="sd">        &gt;&gt;&gt; import matplotlib.pyplot as plt</span>
<span class="sd">        &gt;&gt;&gt; from cdt.data import load_dataset</span>
<span class="sd">        &gt;&gt;&gt; data, labels = load_dataset(&#39;tuebingen&#39;)</span>
<span class="sd">        &gt;&gt;&gt; obj = GNN()</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; # This example uses the predict() method</span>
<span class="sd">        &gt;&gt;&gt; output = obj.predict(data)</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; # This example uses the orient_graph() method. The dataset used</span>
<span class="sd">        &gt;&gt;&gt; # can be loaded using the cdt.data module</span>
<span class="sd">        &gt;&gt;&gt; data, graph = load_dataset(&quot;sachs&quot;)</span>
<span class="sd">        &gt;&gt;&gt; output = obj.orient_graph(data, nx.Graph(graph))</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; #To view the directed graph run the following command</span>
<span class="sd">        &gt;&gt;&gt; nx.draw_networkx(output, font_size=8)</span>
<span class="sd">        &gt;&gt;&gt; plt.show()</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nh</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">nruns</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">njobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gpus</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">verbose</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
                 <span class="n">train_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">test_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
                 <span class="n">dataloader_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Init the model.&quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">GNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">njobs</span> <span class="o">=</span> <span class="n">SETTINGS</span><span class="o">.</span><span class="n">get_default</span><span class="p">(</span><span class="n">njobs</span><span class="o">=</span><span class="n">njobs</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">gpus</span> <span class="o">=</span> <span class="n">SETTINGS</span><span class="o">.</span><span class="n">get_default</span><span class="p">(</span><span class="n">gpu</span><span class="o">=</span><span class="n">gpus</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">nh</span> <span class="o">=</span> <span class="n">nh</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">nruns</span> <span class="o">=</span> <span class="n">nruns</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">train_epochs</span> <span class="o">=</span> <span class="n">train_epochs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">test_epochs</span> <span class="o">=</span> <span class="n">test_epochs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">SETTINGS</span><span class="o">.</span><span class="n">get_default</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dataloader_workers</span> <span class="o">=</span> <span class="n">dataloader_workers</span>

<div class="viewcode-block" id="GNN.predict_proba"><a class="viewcode-back" href="../../../../causality.html#cdt.causality.pairwise.GNN.predict_proba">[docs]</a>    <span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Run multiple times GNN to estimate the causal direction.</span>

<span class="sd">        Args:</span>
<span class="sd">            dataset (torch.utils.data.Dataset or tuple): pair (x, y) to</span>
<span class="sd">               classify. Either a tuple or a torch dataset.</span>

<span class="sd">        Returns:</span>
<span class="sd">            float: Causal score of the pair (Value : 1 if a-&gt;b and -1 if b-&gt;a)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">Dataset</span><span class="p">):</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">dataset</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">th</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">th</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
                       <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">]</span>

        <span class="n">AB</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">BA</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpus</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">result_pair</span> <span class="o">=</span> <span class="n">parallel_run</span><span class="p">(</span><span class="n">GNN_instance</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">njobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">njobs</span><span class="p">,</span>
                                       <span class="n">gpus</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gpus</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span>
                                       <span class="n">train_epochs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">train_epochs</span><span class="p">,</span>
                                       <span class="n">test_epochs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">test_epochs</span><span class="p">,</span>
                                       <span class="n">nruns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nruns</span><span class="p">,</span>
                                       <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
                                       <span class="n">dataloader_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataloader_workers</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">result_pair</span> <span class="o">=</span> <span class="p">[</span><span class="n">GNN_instance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">SETTINGS</span><span class="o">.</span><span class="n">default_device</span><span class="p">,</span>
                                        <span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span>
                                        <span class="n">train_epochs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">train_epochs</span><span class="p">,</span>
                                        <span class="n">test_epochs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">test_epochs</span><span class="p">,</span>
                                        <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
                                        <span class="n">dataloader_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dataloader_workers</span><span class="p">)</span>
                           <span class="k">for</span> <span class="n">run</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nruns</span><span class="p">)]</span>
        <span class="n">AB</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">runpair</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">runpair</span> <span class="ow">in</span> <span class="n">result_pair</span><span class="p">])</span>
        <span class="n">BA</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">runpair</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">runpair</span> <span class="ow">in</span> <span class="n">result_pair</span><span class="p">])</span>

        <span class="n">score_AB</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">AB</span><span class="p">)</span>
        <span class="n">score_BA</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">BA</span><span class="p">)</span>

        <span class="k">return</span> <span class="p">(</span><span class="n">score_BA</span> <span class="o">-</span> <span class="n">score_AB</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">score_BA</span> <span class="o">+</span> <span class="n">score_AB</span><span class="p">)</span></div>

<div class="viewcode-block" id="GNN.orient_graph"><a class="viewcode-back" href="../../../../causality.html#cdt.causality.pairwise.GNN.orient_graph">[docs]</a>    <span class="k">def</span> <span class="nf">orient_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df_data</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">printout</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Orient an undirected graph using the pairwise method defined by the subclass.</span>

<span class="sd">        The pairwise method is ran on every undirected edge.</span>

<span class="sd">        Args:</span>
<span class="sd">            df_data (pandas.DataFrame or MetaDataset): Data (check cdt.utils.io.MetaDataset)</span>
<span class="sd">            graph (networkx.Graph): Graph to orient</span>
<span class="sd">            printout (str): (optional) Path to file where to save temporary results</span>

<span class="sd">        Returns:</span>
<span class="sd">            networkx.DiGraph: a directed graph, which might contain cycles</span>

<span class="sd">        .. note::</span>
<span class="sd">           This function is an override of the base class, in order to be able</span>
<span class="sd">           to use the torch.utils.data.Dataset classes</span>

<span class="sd">        .. warning::</span>
<span class="sd">           Requirement : Name of the nodes in the graph correspond to name of</span>
<span class="sd">           the variables in df_data</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">):</span>
            <span class="n">edges</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span> <span class="k">if</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">edges</span><span class="p">())]</span>
            <span class="n">oriented_edges</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span> <span class="k">if</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">edges</span><span class="p">())]</span>
            <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
                <span class="k">if</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">edges</span><span class="p">()):</span>
                    <span class="n">edges</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">oriented_edges</span><span class="p">:</span>
                <span class="n">output</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="o">*</span><span class="n">i</span><span class="p">)</span>

        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">):</span>
            <span class="n">edges</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Data type not understood.&quot;</span><span class="p">)</span>

        <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">df_data</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">):</span>
            <span class="n">var_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df_data</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">df_data</span><span class="p">,</span> <span class="n">MetaDataset</span><span class="p">):</span>
            <span class="n">var_names</span> <span class="o">=</span> <span class="n">df_data</span><span class="o">.</span><span class="n">get_names</span><span class="p">()</span>

        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">edges</span><span class="p">):</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">df_data</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">):</span>
                <span class="n">dataset</span> <span class="o">=</span> <span class="p">(</span><span class="n">th</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">df_data</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">))</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
                           <span class="n">th</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">df_data</span><span class="p">[</span><span class="n">b</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">))</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
                <span class="n">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="n">idx</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">df_data</span><span class="p">,</span> <span class="n">MetaDataset</span><span class="p">):</span>
                <span class="n">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">df_data</span><span class="o">.</span><span class="n">dataset</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
                                            <span class="n">idx</span><span class="o">=</span><span class="n">idx</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Data type not understood.&quot;</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">weight</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># a causes b</span>
                <span class="n">output</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
            <span class="k">elif</span> <span class="n">weight</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">output</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="nb">abs</span><span class="p">(</span><span class="n">weight</span><span class="p">))</span>
            <span class="k">if</span> <span class="n">printout</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;-&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">b</span><span class="p">),</span> <span class="n">weight</span><span class="p">])</span>
                <span class="n">DataFrame</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;SampleID&#39;</span><span class="p">,</span> <span class="s1">&#39;Predictions&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
                    <span class="n">printout</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">var_names</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">node</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">nodes</span><span class="p">():</span>
                <span class="n">output</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">output</span></div></div>
</pre></div>

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